Your data center network probably is not ready if it was designed more than three years ago and you are running AI workloads, heavy cloud integration, or high performance applications.

Most networks were built for traditional business applications that behaved predictably and moved moderate amounts of data.

Modern workloads generate completely different traffic patterns that old network designs simply cannot handle efficiently.

The result shows up as slow application performance, underutilized expensive servers, and business projects that take longer than they should.

How to Know If Your Network Is the Problem

The easiest way to spot network bottlenecks is watching your expensive equipment sit idle.

Key Warning Signs

GPU servers drop to 50–60% utilization during AI training

Large dataset transfers take hours instead of minutes

Applications slow down unpredictably during peak usage

Cloud apps feel sluggish despite strong internet bandwidth

These issues usually point to network limitations rather than compute or storage problems.

What Changed and Why Old Networks Struggle

Networks designed even five years ago were built for north-south traffic data moving between users and servers.

Modern Reality

Today’s workloads generate east-west traffic, where data flows between servers inside the data center.

Examples include:

AI training workloads

distributed databases

microservices architectures

In modern environments:

70% to 80% of traffic stays internal

older networks lack internal bandwidth

bottlenecks appear in unexpected places

The AI Workload Challenge

Artificial intelligence workloads push networks to their limits.

Why AI Stresses Networks

datasets are often terabytes in size

continuous synchronization between GPU nodes

latency compounds across thousands of iterations

Real Impact

jobs that should finish in hours take much longer

GPUs remain idle waiting for data

infrastructure investment is underutilized

Cloud Integration Creates New Problems

Hybrid environments introduce additional complexity.

Common Issues

inconsistent performance over public internet

increased data transfer (egress) costs

higher latency between environments

Applications split between on-premise and cloud systems depend heavily on network efficiency.

What Modern Networks Actually Need

Modern workloads require a fundamentally different approach to networking.

Key Requirements

High bandwidth everywhereNot just core layers, but across all connections

Spine-leaf architectureReduces hops and improves latency

Balanced traffic designEqual focus on east-west and north-south traffic

Direct cloud interconnectsBetter performance, security, and cost efficiency

Advanced congestion managementPrevents packet loss and retransmissions

The Business Impact You Cannot Ignore

Network bottlenecks directly affect business outcomes.

Key Consequences

Wasted infrastructure investmentServers and GPUs operate below capacity

Delayed innovationAI and product development slow down

Inconsistent application performanceImpacts user experience and productivity

Limited scalabilityGrowth is restricted by infrastructure limits

Practical Steps to Assess Your Situation

What You Should Evaluate

bandwidth utilization across all network links

saturated vs underutilized connections

traffic patterns (east-west vs north-south)

latency between key systems

cloud connectivity performance

Understanding your current state is the first step toward improvement.

When Upgrades Work Versus When You Need Redesign

Upgrades Are Enough If:

only specific links are saturated

overall architecture is still efficient

Redesign Is Required If:

network uses outdated three-tier architecture

performance degrades as workloads scale

east-west traffic overwhelms the system

Incremental fixes cannot solve structural limitations.

What This Means for Planning

Network infrastructure should be part of strategic planning not an afterthought.

Planning Considerations

include network needs in AI and cloud projects

allocate realistic budgets for modernization

consider phased upgrades to reduce disruption

Ignoring network limitations leads to higher long-term costs.

Conclusion

A data center network designed for traditional applications creates bottlenecks that waste infrastructure investment and slow business performance in modern environments.

AI workloads, cloud integration, and distributed systems require networks built for high internal traffic, low latency, and scalable performance.

Addressing these challenges ensures your infrastructure supports growth instead of limiting it.

FAQs

Q.1 How can I tell if network problems are causing slow applications?

Ans. Monitor resource utilization.

If compute and storage have headroom while network links are saturated, the network is the bottleneck.

Performance improving during off-peak hours is another strong indicator.

Q.2 What bandwidth do modern workloads actually need?

Ans.

AI workloads: 100–400 Gbps

general workloads: 25–100 Gbps

Exact needs depend on workload type, but they far exceed older standards.

Q.3 Can software changes fix network bottlenecks?

Ans. Software can optimize traffic, but it cannot replace missing bandwidth.

A combination of hardware upgrades and software optimization delivers the best results.

Q.4 How disruptive is upgrading a data center network?

Ans.

phased upgrades → less disruption, slower results

full redesign → more disruption, faster resolution

Planning minimizes impact in both cases.

Q.5 Is a data center network upgrade worth the cost?

Ans.

Yes when you factor in:

wasted compute capacity

delayed projects

reduced competitiveness

Huawei brings Global Experts to face Challenges of Data Center Network

The Huawei Data Center Network Online Summit 2020, was held to attract experts from around the world to discuss  the value of next-generation data centers for the intelligent era, in which data is a key production factor. Held online and organized by Huawei, the Summit brought together global and regional experts to address the challenges faced by data centers in tomorrow’s hyper-connected…

Fun Fact: At a recent networking conference we attended, a poll was taken and it was discovered that more people in the room knew what country Oslo was the capital of, than those that knew how VxLAN works.

Why VXLAN was Created

In all traditional data centers, VLANs (virtual local area networks) are used to enforce Layer 2 isolation. Over the past few decades, as data centers grew increasingly in size and technology, the need for extending Layer 2 networks across racks within a data center, and even across data centers of different geographical locations, grew. As a result, the limitations of VLANs made this task difficult to accomplish.

In data centers and multi-tenancy environments, thousands of VLANs are needed. The limitation of 4094 VLANs that currently exist is not sufficient.

Using STP (spanning-tree protocol) to provide loop-free networks in return disables most redundant links limiting bandwidth growth.

VLANs are also restrictive in terms of distance and deployment. Routers do not extend Layer 2 broadcast, which leaves the engineer the traditional option of trunking the desired VLAN(s) through multiple switches and increasing the risk of potential network disaster.

VXLAN (Virtual Extensible LAN) addresses all the above limitations that engineers are faced with when using regular VLANs.

 VXLAN Overview

VXLAN is a Layer 2 overlay deployment over an existing Layer 3 network. It uses MAC Address-in-User Datagram Protocol (MAC-in-UDP) encapsulation to provide a means to extend Layer 2 segments across the data center network. VXLAN is designed to not only provide the same services and core functions that VLANs do today, but also provide a solution to extend the Layer 2 network over an existing Layer 3 network. This is called an “overlay”. An overlay network is a virtual network that is built on top of an existing Layer 2/Layer 3 network. VXLAN makes it easier for network engineers to scale a data center or cloud computing environment, while still being able to isolate tenant services.

In a multi-tenant environment, each tenant requires its own logical network, which in turn, requires its own Network Identification (NID). Traditionally, network engineers have used VLANs to accomplish the task of separating applications and tenants but with the scale limitation of a regular VLAN, only 4096 unique NIDs could be deployed at a given time. However, VXLAN extends the VLAN address space by adding a 24-bit segment and increasing the number of available NIDs (called VNIs) from 4094 to 16 million. This allows for millions of isolated Layer 2 VXLAN networks to co-exist on a common Layer 3 infrastructure.

VXLAN Benefits

Flexible placement of multi-tenant segments throughout the data center. It provides a solution to extend Layer 2 segments over the underlying shared network infrastructure so that tenant workload     can be placed across physical segments in the data center.

Higher scalability to address more Layer 2 segments. VLANs use a 12-bit VLAN ID to address Layer 2 segments, which results in limiting scalability of only 4094 VLANs. VXLAN uses a 24-bit segment ID  which enables up to 16 million VXLAN segments to coexist in the same     common network infrastructure.

Better utilization of available     network paths in the underlying infrastructure. VLAN uses the Spanning-Tree Protocol for loop     prevention, which ends up disabling half of the network links in a network     by blocking redundant paths. In contrast, VXLAN packets are transferred     through the underlying network based on its Layer 3 header and can take     complete advantage of Layer 3 routing, equal-cost multipath (ECMP)     routing, and link aggregation protocols to use all available paths.

India Data Center Market size is projected to reach USD 1.5 billion by 2022 from USD 1.0 billion in 2018, growing at a Compound Annual Growth Rate (CAGR) of 11.4% during the forecast period (2018–2022). Major growth factors for the market are deeper internet penetration, increase in digital data traffic, public cloud services, and higher expected growth for IoT.

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